Modeling computer network topology based on dynamic usage relationships

ABSTRACT

A method for modeling dependencies in a computing system including a plurality of resources, the method includes generating usage data based on data communications in a computing environment between a first resource and a second resource; determining resource relationship data for the first resource and the second resource including the usage data; and computing a measure of bonding between the first resource and the second resource based, at least in part, on recency of the data communications.

BACKGROUND OF THE INVENTION

The present disclosure relates generally to the field of systemsmanagement, and more particularly to modeling dependencies in anetworked system.

It is known to model relationships among resources in a computernetwork. It is further known to model dependencies among resources witha view towards enabling failover in case a resource is inaccessible.

In the context of systems management, the existence of andinter-connections between different network resources is generallymodeled using a resource relationships graph, also known as a topologygraph, a topology map, or a dependency graph. Multiple topologyperspectives could be derived for every resource of interest to theuser. Each such topology perspective displays the primary resource ofinterest along with the other resources it is related to and therelationships that exists between them. For example, a network topologyperspective displays the network connections that exist between aresource and the network elements to which it is connected. As a furtherexample, a network topology virtualization perspective displays thenetwork connections that exist between a resource and the virtualsystems to which it is linked.

It is known to provide the user with different topology perspectives,where each different perspective displays a corresponding set ofrelationships applicable to an “endpoint.”

SUMMARY

Embodiments of the invention relate to a system, a computer programproduct and a method for modeling dependencies in a computing systemincluding a plurality of resources that includes: generating usage databased on data communications in a computing environment between a firstresource and a second resource; determining resource relationship datafor the first resource and the second resource including the usage data;and computing a measure of bonding between the first resource and thesecond resource based, at least in part, on recency of the datacommunications.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a schematic view of a first embodiment of a networkedcomputers system (that is, a system including one or more processingdevices) according to the present invention;

FIG. 2 is a first flowchart showing a process performed, at least inpart, by the first embodiment computers system;

FIG. 3A is a schematic view of a portion of the first embodimentcomputers system;

FIG. 3B is a screenshot generated by the first embodiment computerssystem;

FIG. 4 is a second flowchart showing a process performed, at least inpart, by the first embodiment computers system; and

FIG. 5 is a schematic view of a second embodiment of a networkedcomputers system according to an embodiment of the present disclosure.

DETAILED DESCRIPTION

This Detailed Description section is divided into the followingsub-sections: (i) The Hardware and Software Environment; (ii) FirstEmbodiment; (iii) Further Comments and/or Embodiments; and (iv)Definitions.

I. The Hardware and Software Environment

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer-readablemedium(s) having computer-readable program code/instructions embodiedthereon.

Any combination of computer-readable media may be utilized.Computer-readable media may be a computer-readable signal medium or acomputer-readable storage medium. A computer-readable storage medium maybe, for example, but not limited to, an electronic, magnetic, optical,electromagnetic, infrared, or semiconductor system, apparatus, ordevice, or any suitable combination of the foregoing. More specificexamples (a non-exhaustive list) of a computer-readable storage mediumwould include the following: an electrical connection having one or morewires, a portable computer diskette, a hard disk, a random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), an optical fiber, a portable compactdisc read-only memory (CD-ROM), an optical storage device, a magneticstorage device, or any suitable combination of the foregoing. In thecontext of this document, a computer-readable storage medium may be anytangible medium that can contain, or store a program for use by or inconnection with an instruction execution system, apparatus, or device.

A computer-readable signal medium may include a propagated data signalwith computer-readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electromagnetic, optical, or any suitable combination thereof. Acomputer-readable signal medium may be any computer-readable medium thatis not a computer-readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer-readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object-oriented programming languagesuch as Java (note: the term(s) “Java” may be subject to trademarkrights in various jurisdictions throughout the world and are used hereonly in reference to the products or services properly denominated bythe marks to the extent that such trademark rights may exist),Smalltalk, C++ or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The program code may execute entirely on a user's computer,partly on the user's computer, as a stand-alone software package, partlyon the user's computer and partly on a remote computer or entirely onthe remote computer or server. In the latter scenario, the remotecomputer may be connected to the user's computer through any type ofnetwork, including a local area network (LAN) or a wide area network(WAN), or the connection may be made to an external computer (forexample, through the Internet using an Internet Service Provider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in acomputer-readable medium that can direct a computer, other programmabledata processing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer-readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce acomputer-implemented process such that the instructions which execute onthe computer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

An embodiment of a possible hardware and software environment forsoftware and/or methods according to the present invention will now bedescribed in detail with reference to the Figures. FIG. 1 is afunctional block diagram illustrating various portions of networkedcomputers system 100, including: dynamic relationship factor (DRF)computer sub-system (that is, a portion of the larger computer systemthat itself includes a computer) 102; first host computer sub-system104; R3 virtual machine sub-system (referred to as R3) 606; R4 virtualmachine sub-system (referred to as R4) 608; second host computersub-system 106; R5 virtual machine sub-system (referred to as R5) 610;R1 computer sub-system used by user U1 (referred to as U1,R1) 602; R2computer sub-system used by user U2 (referred to as U2,R2) 604;communication network 114; DRF computer 200; communication unit 202;processor set 204; input/output (i/o) interface set 206; memory device208; persistent storage device 210; display device 212; external deviceset 214; random access memory (RAM) devices 230; cache memory device232; program 300; monitoring module 355; calculation module 360; anddependency module 365.

Server computer sub-system 102 is, in many respects, representative ofthe various computer sub-system(s) in the present invention.Accordingly, several portions of computer sub-system 102 will now bediscussed in the following paragraphs.

Server computer sub-system 102 may be a laptop computer, a tabletcomputer, a net book computer, a personal computer (PC), a desktopcomputer, a personal digital assistant (PDA), a smart phone, or anyprogrammable electronic device capable of communicating with the clientsub-systems via network 114. Program 300 is a collection of machinereadable instructions and/or data that is used to create, manage andcontrol certain software functions that will be discussed in detail,below, in the First Embodiment sub-section of this Detailed Descriptionsection.

Server computer sub-system 102 is capable of communicating with othercomputer sub-systems via network 114 (see FIG. 1). Network 114 can be,for example, a local area network (LAN), a wide area network (WAN) suchas the Internet, or a combination of the two, and can include wired,wireless, or fiber optic connections. In general, network 114 can be anycombination of connections and protocols that will supportcommunications between server and client sub-systems.

It should be appreciated that FIG. 1 provides only an illustration ofone implementation (that is, system 100) and does not imply anylimitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironment may be made, especially with respect to current andanticipated future advances in cloud computing, distributed computing,smaller computing devices, network communications and the like.

Server computer sub-system 102 is shown as a block diagram with manydouble arrows. These double arrows (no separate reference numerals)represent a communications fabric, which provides communications betweenvarious components of sub-system 102. This communications fabric can beimplemented with any architecture designed for passing data and/orcontrol information between processors (such as microprocessors,communications and network processors, etc.), system memory, peripheraldevices, and any other hardware components within a system. For example,the communications fabric can be implemented, at least in part, with oneor more buses.

Memory 208 and persistent storage 210 are computer-readable storagemedia. In general, memory 208 can include any suitable volatile ornon-volatile computer-readable storage media. It is further noted that,now and/or in the near future: (i) external device(s) 214 may be able tosupply, some or all, memory for sub-system 102; and/or (ii) devicesexternal to sub-system 102 may be able to provide memory for sub-system102.

Program 300 is stored in persistent storage 210 for access and/orexecution by one or more of the respective computer processors 204,usually through one or more memories of memory 208. Persistent storage210: (i) is at least more persistent than a signal in transit; (ii)stores the device on a tangible medium (such as magnetic or opticaldomains); and (iii) is substantially less persistent than permanentstorage. Alternatively, data storage may be more persistent and/orpermanent than the type of storage provided by persistent storage 210.

Program 300 may include both machine readable and performableinstructions and/or substantive data (that is, the type of data storedin a database). In this particular embodiment, persistent storage 210includes a magnetic hard disk drive. To name some possible variations,persistent storage 210 may include a solid state hard drive, asemiconductor storage device, a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM), a flash memory, or any othercomputer-readable storage media that is capable of storing programinstructions or digital information.

The media used by persistent storage 210 may also be removable. Forexample, a removable hard drive may be used for persistent storage 210.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer-readable storage medium that is also part of persistent storage210.

Communications unit 202, in these examples, provides for communicationswith other data processing systems or devices external to sub-system102, such as client sub-systems 104, 106, 108, 110 and virtual machine112. In these examples, communications unit 202 includes one or morenetwork interface cards. Communications unit 202 may providecommunications through the use of either or both physical and wirelesscommunications links. Any software modules discussed herein may bedownloaded to a persistent storage device (such as persistent storagedevice 210) through a communications unit (such as communication unit202).

I/O interface set 206 allows for input and output of data with otherdevices that may be connected locally in data communication with servercomputer 200. For example, I/O interface set 206 provides a connectionto external device set 214. External device set 214 will typicallyinclude devices such as a keyboard, a keypad, a touch screen, and/orsome other suitable input device. External device set 214 can alsoinclude portable computer-readable storage media such as, for example,thumb drives, portable optical or magnetic disks, and memory cards.Software and data used to practice embodiments of the present invention,for example, program 300, can be stored on such portablecomputer-readable storage media. In these embodiments the relevantsoftware may (or may not) be loaded, in whole or in part, ontopersistent storage device 210 via I/O interface set 206. I/O interfaceset 206 also connects in data communication with display device 212.

Display device 212 provides a mechanism to display data to a user andmay be, for example, a computer monitor or a smart phone display screen.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

II. First Embodiment

Preliminary note: The flowchart and block diagrams in the followingFigures illustrate the architecture, functionality, and operation ofpossible implementations of systems, methods and computer programproducts according to various embodiments of the present invention. Inthis regard, each block in the flowchart or block diagrams may representa module, segment, or portion of code, which comprises one or moreexecutable instructions for implementing the specified logicalfunction(s). It should also be noted that, in some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

FIG. 2 shows a flow chart 250 depicting a method according to thepresent invention. FIG. 1 shows program 300 for performing at least someof the method steps of flow chart 250. This method and associatedsoftware will now be discussed, over the course of the followingparagraphs, with extensive reference to FIG. 2 (for the method stepblocks) and FIG. 1 (for the software blocks).

Processing begins at step S255 where monitoring module (“mod”) 355monitors computing system 600 (see FIG. 3A) to detect datacommunications among and between its users and/or resources (which are:first user/resource 602, second user/resource 604, third resource 606,fourth resource 608, and fifth resource 610). As further shown in FIG.3A, the data communications made during the monitoring period of stepS255 are: C1, C2, C3, C4, C5, C6, C7, C8, C9 and C10. In thisembodiment, the computer (DRF computer 200) and software (mod 355)actually performing this monitoring are not part of computing system600, which is being monitored (although DRF computer 200 is in datacommunication with computing system 600 in order to allow the monitoringto take place). Alternatively, DRF computer 200 could be part of thecomputing system that it monitors and for which it models dependencies.

Processing proceeds to step S260, where calculation mod 360 calculatesdynamic relationship factors (“DRFs”) based on the monitored datacommunications of step S255. The monitored data communications (shown inFIG. 3A) and associated DRFs are shown in the following Table 1:

VOL- FRE- PAIR COMMNICATION UME TIME QUENCY DRF1 DRF2 602/604 C4 100 7 114.3 14.3 602/606 C1 100 11 3 9.1 327.0 602/606 C5 200 6 3 33.3 327.0602/606 C8 200 3 3 66.7 327.0 602/608 C3 100 8 1 12.5 12.5 604/608 C9100 2 1 50.0 50.0 604/610 C10 100 1 1 100.0 100.0 606/608 C6 100 5 120.0 20.0 608/610 C2 500 9 2 55.6 361.0 608/610 C7 500 4 2 125.0 361.0Note: pairs with no data communications between them are not listed herefor brevity. In Table 1: (i) the DRF1 values are based on data volume ofeach communication and how much time has passed since the communicationwas made (that is, the TIME column of Table 10); (ii) the DRF1 valuesare calculated for each communication by dividing volume by time; (iii)the DRF2 values are based on data volume of all communications for apair, how much time has passed since various communication(s) betweenmembers of the pair, and communication frequency; and (iv) the DRF2values are calculated for each by summing the DRF1 values for allcommunication(s) between the pair, and then multiplying by the number ofcommunications between the pair. This particular method of calculatingDRF values is not necessarily the best way to calculate DRF valuesaccording to the present invention, but is explained here to helpreaders understand how DRF values can be based on volume, frequency andtime elapsed.

Processing proceeds to step S270, where dependency mod 365 modelsdependencies by determining that all pairs in system 600 that have amonitored data communication (see Table 1, above, where all listed pairsin system 600 do have at least one communication therebetween.Processing proceeds to step S275 where mod 365 goes on to generate a setof topology graphs (from various perspectives) and to display the samefor an interested party through display device 212. One such networktopology is shown at screenshot 650 of FIG. 3B.

FIG. 3B is screenshot 650 showing a topology graph of computing system600 as displayed to the user according to one embodiment of the presentdisclosure. Alternatively, the topology graph is stored in a file, orotherwise made available for viewing and/or analysis. In screenshot 650,the user is displayed the DRF2 values from the perspective of U1,R1 602for usage relationships according to communications between resourceand/or user pairs within the computing system. The DRFs for datacommunications C6 and C9 are not shown because communications C6 and C9are not seen from the U1,R1 perspective. In this embodiment, where adirect dependency is shown, any corresponding transitive dependenciesare not shown. Alternatively, every transitive dependency is shown,despite there also being a direct dependency to the same node.

It should be noted that the transitive dependency between U1,R1 602 andR5 610 is shown in this topology graph. There are two dependency chainsthrough which U1,R1 602 and R5 610 have a transitive dependency: (i)through R4, 608; and (ii) through U2,R2 604. Alternatively, transitivedependencies are not shown on the topology graph.

III. Further Comments and/or Embodiments

Some embodiments of the present invention address how to model dynamicdependency relationships, or dynamic usage relationships, among computernetwork resources and the users of those resources.

Some embodiments of the present invention provide a system and method toensure that the dynamic real-time relationships among resources, as wellas the users using the resources, are continuously maintained andupdated to reflect changes to the resources. To achieve this, someembodiments calculate a dynamic relationship factor (DRF). Additionally,some embodiments of the present invention continuously update the DRFwith current network resource availability. The DRF measures bondingamong resources and users in a network including: (i) resource toresource bonding; (ii) user to resource bonding; and/or (iii) user touser bonding.

Some embodiments of the present invention model transitive dependenciesamong resources and users in a computer network. Transitive dependenciesare those dependencies that extend beyond a first layer of dependencies.For example, node R1 is dependent on node R2, which is in turn dependenton R3. R1 is said to have a transitive dependency on R3.

Some embodiments of the present invention recognize that: (i) the staticview of conventional network topologies do not represent the actual realtime interaction relationships and hence dependency among the resources;(ii) there is a need for a model to represent these usage relationshipsand display them to the users on demand; and (iii) conventional networktopologies do not provide dynamic usage resource relationships, forexample, there is generally no way of determining those resources thatexecute processes jointly with the resource in question (mentionedabove, discussed in more detail, below, as “transitive dependency”).

Some embodiments of the present invention provide a method and apparatusfor modeling computer network topology based on dynamic resource usagerelationships. Usage relationships represent the true picture of how thenetwork's resources are being used collectively. These usagerelationships also provide valuable insight to network administrators toredesign the network. For example, usage relationships provide thenecessary support for moving related resources into the same subnet orserver pool.

Some embodiments of the present invention provide a system and methodfor: (i) calculating the degree, or measure, of bonding between systemsin a cloud and/or computer network; (ii) modeling the resource usagerelationship data; and (iii) displaying usage relationship data and thedegree of bonding between systems in a cloud and/or computer network toreflect how systems interact with each other in real time. In this way,the real time degree of dependencies, or measure of bonding, amonginter-connected resources and/or users is accurately known and isdynamically updated.

FIG. 4 is a flow chart of process 400 providing a method to ensure thatthe dynamic real time usage relationships among resources and/or usersare continuously maintained and updated based on resource changes.

Processing begins at step S402, where the resources that are used by auser are determined.

Processing proceeds to step S404, where usage relationships among everypair of resources are modeled on a scale of 0-1, with the higher numberdenoting closer bonding between the resources. The value assigned to theusage relationship is derived by monitoring how frequently the pair ofresources interacts (communicates and exchanges data) with each other inthe network.

Processing proceeds to step S406, where the closest bonded resourcesand/or users within the cloud and/or computer network are determinedbased on resource to resource, user to resource, and user to usermappings.

The DRF measures the extent of bonding between a pair of resourcesand/or users. Resource to resource bonding varies in degree andincludes: (i) a first resource, R1, needs a second resource, R2, inorder to execute a program; (ii) a first resource, R1, would be affectedif a second resource, R2, went down, but resource R2 is replaceable;(iii) a first resource, R1, would be affected if a second resource, R2,went down, but resource R2 is not replaceable (for example, R2 is acritical resource); and (iv) a second resource, R2, is a back-up, ormirror, for a first resource, R1, such that if resource R2 is down, thenormal functioning of resource R1 is not affected; however, resourceR1's HA (high availability) capability or scalability may be affected.

User to resource bonding arises when a first user, U1, needs to access afirst resource, R1, as part of user U1's activities. Bonding betweenuser U1 and resource R1 varies in degree and includes: (i) user U1 needsresource R1 in order to execute a program; (ii) user U1 would beaffected if resource R1 went down, but resource R1 is replaceable; (iii)user U1 would be affected if resource R1 went down, but resource R1 isnot replaceable (for example, R1 is a critical resource); and (iv) userU1 can perform the same function without resource R1, but with lowperformance or with risk of outage due to lack of high availability.

Bonding between a pair of resources and/or users is measured on a scaleof 0-1, with the value “1” denoting a critical dependence, or bond.Transitive dependencies are defined between three resources and/orusers, such as R1, R2 and R3. For example, where the dependency of R1 onR2 is d12 and the dependency of R2 on R3 is d23, and α represents aweighting value where one interceding dependency is closer than theother interceding dependency. Where no weighting is desired, the valueof a is simply the quotient of 1 divided by the number of intercedingdependencies. To explain further, α denotes the weight of a dependency.As mentioned in the above example, α will have a higher value when thedependency is critical in the sense that if the linkage is broken, thesystem, application, and/or user cannot perform the intended function inthe computing infrastructure. Accordingly, the transitive dependency ofR1 on R3, where R1 is dependent on R2, and R2 is dependent on R3, iscalculated as α12*d12+α23*d23, where α12+α23=1. Similar transitivedependencies are defined for any pair of resources connected togethervia a dependency chain.

The value of the DRF can be derived from one or more of the followingfactors, but not limited to these factors: (i) the number ofinteractions between two resources or a resource and a user in a cloudenvironment in a defined timeframe, i.e. frequency of interaction(DRF=>fn(freq)); (ii) the average duration of each interaction betweenresources and/or users (DRF=>fn(Avg Time of Interaction)); (iii) theamount of data exchanged between the resources and/or users (DRF=>fn(MBof Data exchanged)); and (iv) the recency of data exchanged between theresources and/or users (DRF=>fn(t), where the term “t” is the durationof the most recent data exchange between the resources and/or users inquestion.

FIG. 5 is a schematic view of sub-systems in cloud and computer networksystem 500 according to an embodiment of the present disclosure. System500 includes: users 502, 504, and 506; network administrator 508;virtual machines 510, 512, 514; servers 516, 518; and switch 520. Thecommunication lines shown in solid lines indicate tight couplings, ordirect dependencies, and the dashed lines indicate transitivedependencies. It should be noted that for the network topology the userto user dependencies are all transitive dependencies because useractivity is directed to the network interface and not to each other.That is, the user-user dependency is weighted as described above fortransitive dependencies.

Dependencies with respect to dynamic usage relationships for resourceand/or user pairs in system 500 will now be discussed. User 502 usesvirtual machines 510 and 512 for her activities. Accordingly, the DRFvalue between user 502 and the two virtual machines is very high, suchas 1, to indicate a critical dependence of the user on each of theseresources. This tight coupling between user 502 and virtual machines 510and 512 is shown in the figure as a solid line. Similarly, the virtualmachines are each tightly coupled to server computer 516, alsoindicating a critical dependence.

Virtual machine 512 is shown as having a transitive dependency onnetwork switch 520. Virtual machine 512 has a critical dependency onserver 516 and server 516 has a critical dependency on the networkswitch, so virtual machine 512 has a transitive dependency on thenetwork switch. This transitive dependency between virtual machine 512and switch 520 described above is expressed mathematically as:α₅₁₂₋₅₁₆*d₅₁₂₋₅₁₆+α₅₁₆₋₅₂₀d₅₁₆₋₅₂₀,where α₅₁₂₋₅₁₆+α₅₁₆₋₅₂₀=1

Server 516 has a transitive dependency on network administrator 508, whooperates the switch. The network administrator uses the switch in heractivities, so there is a critical dependency between the administratorand the switch. If the administrator shuts down the switch, server 516,which is also dependent on the switch, is affected.

User 502 has a critical dependency on virtual machines 510 and 512. User502 has a weak transitive dependency on the physical server 516. Thistransitive dependency on physical server 516, which is hosting thevirtual machines, is weak because, in the event of server 516 failing,the virtual machines can be migrated to another host server. Theweakness or strength of a dependency is understood by the weightingfactor, “a,” as discussed above.

The actions of users 502 and 504 do not affect one another, so the userpair 502-504 has a DRF close to zero, indicating little to nodependence. For a large, distributed and interconnected computingenvironment, especially with virtual entities and corresponding dynamism(of migration, addition and removal of endpoints), there is aprobability of transient or very remote dependencies on variousendpoints and/or users which may not be very apparent.

User 504 has a transitive dependency on server 518. That is, user 504uses virtual machine 514 for her activities, so there is a criticaldependency between user 504 and virtual machine 514. Virtual machine 514has a critical dependency on server 518. If server 518 is shut down,virtual machine 514 goes down and the activities of user 504 areimpacted.

In this network topology, user 504 may be affected by actions taken byuser 506 so, there is a transitive dependency between users 504 and 506.User 506 uses server 518 for her activities, so there is a criticaldependency shown in the illustration.

Some embodiments of the present invention enhance the topology view ofany cloud setup to depict how tightly or loosely resources and/or usersare related to each other.

Some embodiments of the present invention use DRF to derive usefulconclusions about a cloud setup including: (i) if a resource was to godown, which users and other resources would be affected in the cloud;(ii) how are various resources related directly or indirectly in a cloudeco-system; (iii) based on DRF, the placement of resources (physical andvirtual) may be optimized (for example, two resources having a high(close to 1) DRF should be placed in the same subnet and the samegeographic location to reduce network latency) (for another example,virtual resources, while migrating, should be migrated together forco-location if they have high (close to 1) DRF for each other).

Some embodiments of the present invention provide one or more of thefollowing features, characteristics, and/or advantages: (i) a system andmethod to model topology and relationships between resources (physicaland virtual) in a cloud, based on real time interaction among theresources and/or users; (ii) a system and method to model topology andrelationships between users and resources (physical and virtual) in acloud, based on real time interaction; (iii) a method to automaticallyrecalculate resource-resource, user-user, and/or resource-userrelationships in a cloud and/or data center when a resource or a userchanges (roles, behaviors, and etc.) based on real time interactionbetween the resources and/or users in the network; (iv) a method toperform analytics in a cloud computing environment leveraging one ormore features, characteristics, and/or advantages noted above, based onheuristics of interaction (in real-time) between resources and users.

Some embodiments of the present invention provide one or more of thefollowing features, characteristics, and/or advantages: (i) a mechanismfor modeling topology of cloud setup as per dynamic interactionrelationships between resources in real-time; (ii) ensuring that theusage relationships of value to the user are always accurate and can beautomatically retrieved on demand; (iii) easily integrated with anyproduct that has a need to view the topology of a network from multipleperspectives; (iv) displaying a perspective of the computer networkbased on the combination of topology information from the network andrelationships among the network's resources based on dynamic usage; and(v) using the interaction between the systems in a cloud and/or computernetwork to derive the interrelationship between them.

Some embodiments of the present invention derive relationships and theextent of bonding, or relative strength, using various factors which aretypical in a cloud setup on which DRF is based (some of these could beused as inputs as well, to start off). These factors go beyond merenetwork packet sniffing (details in network packets). Some of thesefactors include: (i) virtualization (with its dynamic relationships,where the host-to-virtual machine relationship comes into play, alongwith migration); (ii) accounting for the quantum (frequency/volume) ofdata exchange; (iii) knowing the entities involved in data exchange;and/or (iv) knowing the nature, or characteristics, of data exchanged(such as data exchanged between SAN (storage area network) storage andservers or bulk data replication. For example, data exchanged between aserver and a storage via a switch may appear to have a high dependencybetween server, switch, and storage based on network traffic; however,accounting for the fact that this is regular traffic between a serverand a storage, which is most commonly configured through MPIO(multi-path I/O) configuration, the DRF would not attribute a high, orcritical, dependency due to the specific factors of this case.

Virtual resources add another dimension of dynamicity in therelationships. For example, rather than just mapping the dynamicrelationship between: (i) physical hosts; (ii) services; and/or (iii)applications, some embodiments also map the dynamic relationships ofvirtual resources including: (i) servers; (ii) virtual local areanetworks (VLANs); and/or (iii) storage pools. Some embodiments of thepresent invention include both physical and virtual resources in anetwork. In that way, connections are known for the entire network andhow the network changes when one or more resources change within thenetwork. One example of a network change is where a virtual server ismigrated from one host to another. In some embodiments of the presentinvention, the virtual resource migration is recognized so that packetssent and/or received by this virtual resource are not missed indetermining relationships.

Some embodiments of the present invention provide improved accuracy overderiving relationships based on just packets exchanged in a given timewindow by including data for: (i) recency of data exchanged; and (ii)frequency of data exchanged. For example, the actual data exchanged maybe relatively low, but there are very frequent interactions between theresources and/or users.

While some embodiments of the present invention may be applied to: (i)resource interconnection information; (ii) problem determination; and/or(iii) maintenance mode impact, the accuracy of DFR permits someembodiments to optimize resource placement (both physical and virtualresources). For example, two resources having a high DRF (close to 1)may be placed in the same sub-net and/or geographic location to reducenetwork latency. For another example, virtual resources having a highDRF (close to 1), while migrating, may be migrated together forco-location.

IV. Definitions

Present invention: should not be taken as an absolute indication thatthe subject matter described by the term “present invention” is coveredby either the claims as they are filed, or by the claims that mayeventually issue after patent prosecution; while the term “presentinvention” is used to help the reader to get a general feel for whichdisclosures herein that are believed as maybe being new, thisunderstanding, as indicated by use of the term “present invention,” istentative and provisional and subject to change over the course ofpatent prosecution as relevant information is developed and as theclaims are potentially amended.

Embodiment: see definition of “present invention” above—similar cautionsapply to the term “embodiment.”

and/or: inclusive or; for example, A, B, “and/or” C means that at leastone of A or B or C is true and applicable.

User/subscriber: includes, but is not necessarily limited to, thefollowing: (i) a single individual human; (ii) an artificialintelligence entity with sufficient intelligence to act as a user orsubscriber; and/or (iii) a group of related users or subscribers.

Data communication: any sort of data communication scheme now known orto be developed in the future, including wireless communication, wiredcommunication and communication routes that have wireless and wiredportions; data communication is not necessarily limited to: (i) directdata communication; (ii) indirect data communication; and/or (iii) datacommunication where the format, packetization status, medium, encryptionstatus and/or protocol remains constant over the entire course of thedata communication.

Software storage device: any device (or set of devices) capable ofstoring computer code in a manner less transient than a signal intransit.

Tangible medium software storage device: any software storage device(see Definition, above) that stores the computer code in and/or on atangible medium.

Non-transitory software storage device: any software storage device (seeDefinition, above) that stores the computer code in a non-transitorymanner.

Computer: any device with significant data processing and/ormachine-readable instruction reading capabilities including, but notlimited to: desktop computers, mainframe computers, laptop computers,field-programmable gate array (fpga) based devices, smart phones,personal digital assistants (PDAs), body-mounted or inserted computers,embedded device style computers, and application-specific integratedcircuit (ASIC) based devices.

Usage resource relationship: the relationship among resources from theperspective of usage, that is, the characteristics of actual datacommunications between resources and/or users.

Network resource relationship: the relationship among network connectedresources with respect to the volume of data exchanged, in actual datacommunication(s), between resources.

Virtual resource relationship: the relationship among virtual resources,including virtual machines, with respect to the volume of dataexchanged, in actual data communication(s), between virtual resourcesand/or network resources.

Dynamic usage resource relationship: any usage resource relationshipmetric available within such a reasonable response time as to beconsidered real time availability.

Real time: includes any time frame of sufficiently short duration as toprovide reasonable response time for information processing acceptableto a user of the subject matter described; in other words, any latenciesare sufficiently short in duration such that a user would reactsubstantially the same way as if there was no latency between a change,or event, and the presentation of the change, or event, to the user.

Computing environment: a computing environment will include multiplecomputing resources (called simply “resources”) and may further includeat least one human resource (called a “user,” see definition, above); atleast some of the data communications are made over a set of networksincluding at least one network.

Between: means among and/or between.

What is claimed is:
 1. A method comprising: generating usage data basedon data communications in a computing environment including a pair ofresources made up of a first resource and a second resource, the usagedata including a time of occurrence for each data exchange and aduration of each data exchange; determining resource relationship datafor the first resource and the second resource including a resourceusage data set corresponding to the pair of resources; analyzing theresource relationship data to determine a most recent data communicationbetween the pair of resources according to the time of occurrence foreach data exchange and to identify a first duration corresponding to theidentified most recent data communication; and computing a measure ofbonding between the pair of resources based, at least in part, on thefirst duration of the most recent data communication; generating a setof topology graph(s) based upon the measure of bonding between the pairof resources; and displaying the set of topology graph(s), wherein thetopology graph(s) indicate the measure of bonding between the pair ofresources.
 2. The method of claim 1, wherein: the data communications inthe computing environment occur over a period of time; and a recency ofthe data communications is with respect to an end of the period of time.3. The method of claim 1, further comprising: defining the firstresource and the second resource as a resource pair among a plurality ofresources in the computing environment.
 4. The method of claim 3 whereinthe resource relationship data includes transitive dependencies amongthe plurality of resources including the first resource and the secondresource.
 5. The method of claim 1, wherein: the usage data furtherincludes volume of data communicated in the data communications; andcomputing the measure of bonding is further based on the volume of datacommunicated in the most recent data communication.
 6. The method ofclaim 1, wherein the first resource is a sub-system virtual machine. 7.A computer program product comprising a computer-readable storage mediumhaving a set of instructions stored therein which, when executed by aprocessor, causes the processor to model computer network topology by:generating usage data based on data communications in a computingenvironment including a pair of resources made up of a first resourceand a second resource, the usage data including a time of occurrence foreach data exchange and a duration of each data exchange; determiningresource relationship data for the first resource and the secondresource including a resource usage data set corresponding to the pairof resources; analyzing the resource relationship data to determine amost recent data communication between the pair of resources accordingto the time of occurrence for each data exchange and to identify a firstduration corresponding to the identified most recent data communication;and computing a measure of bonding between the pair of resources based,at least in part, on the first duration of the most recent datacommunication; generating a set of topology graph(s) based upon themeasure of bonding between the pair of resources; and displaying the setof topology graph(s), wherein the topology graph(s) indicate the measureof bonding between the pair of resources.
 8. The computer programproduct of claim 7, wherein: the data communications in the computingenvironment occur over a period of time; and a recency of the datacommunications is with respect to an end of the period of time.
 9. Thecomputer program product of claim 7, further causing model computernetwork topology by: defining the first resource and the second resourceas a resource pair among a plurality of resources in the computingenvironment.
 10. The computer program product of claim 9, wherein theresource relationship data includes transitive dependencies among theplurality of resources including the first resource and the secondresource.
 11. The computer program product of claim 7, wherein: theusage data further includes volume of data communicated in the datacommunications; and computing the measure of bonding is further based onthe volume of data communicated in the most recent data communication.12. The computer program product of claim 7, wherein the first resourceis a sub-system virtual machine.
 13. A computer system comprising: aprocessor set; and a computer readable storage medium; wherein: theprocessor set is structured, located, connected, and/or programmed torun program instructions stored on the computer readable storage medium;and the program instructions which, when executed by the processor set,cause the processor set to model computer network topology by:generating usage data based on data communications in a computingenvironment including a pair of resources made up of a first resourceand a second resource, the usage data including a time of occurrence foreach data exchange and a duration of each data exchange; determiningresource relationship data for the first resource and the secondresource including a resource usage data set corresponding to the pairof resources; analyzing the resource relationship data to determine amost recent data communication between the pair of resources accordingto the time of occurrence for each data exchange and to identify a firstduration corresponding to the identified most recent data communication;and computing a measure of bonding between the pair of resources based,at least in part, on the first duration of the most recent datacommunications; generating a set of topology graph(s) based upon themeasure of bonding between the pair of resources; and displaying the setof topology graph(s), wherein the topology graph(s) indicate the measureof bonding between the pair of resources.
 14. The computer system ofclaim 13, wherein: the data communications in the computing environmentoccur over a period of time; and a recency of the data communications iswith respect to an end of the period of time.
 15. The computer system ofclaim 13, further causing the processor to model computer networktopology by: defining the first resource and the second resource as aresource pair among a plurality of resources in the computingenvironment.
 16. The computer system of claim 15, wherein the resourcerelationship data includes transitive dependencies among the pluralityof resources including the first resource and the second resource. 17.The computer system of claim 13, wherein: the usage data furtherincludes volume of data communicated in the data communications; andcomputing the measure of bonding is further based on the volume of datacommunicated in the most recent data communication.
 18. The computersystem of claim 13, wherein the first resource is a sub-system virtualmachine.